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Module « numpy »

Fonction min - module numpy

Signature de la fonction min

def min(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>) 

Description

help(numpy.min)

Return the minimum of an array or minimum along an axis.

Parameters
----------
a : array_like
    Input data.
axis : None or int or tuple of ints, optional
    Axis or axes along which to operate.  By default, flattened input is
    used.

    If this is a tuple of ints, the minimum is selected over multiple axes,
    instead of a single axis or all the axes as before.
out : ndarray, optional
    Alternative output array in which to place the result.  Must
    be of the same shape and buffer length as the expected output.
    See :ref:`ufuncs-output-type` for more details.

keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

    If the default value is passed, then `keepdims` will not be
    passed through to the ``min`` method of sub-classes of
    `ndarray`, however any non-default value will be.  If the
    sub-class' method does not implement `keepdims` any
    exceptions will be raised.

initial : scalar, optional
    The maximum value of an output element. Must be present to allow
    computation on empty slice. See `~numpy.ufunc.reduce` for details.

where : array_like of bool, optional
    Elements to compare for the minimum. See `~numpy.ufunc.reduce`
    for details.

Returns
-------
min : ndarray or scalar
    Minimum of `a`. If `axis` is None, the result is a scalar value.
    If `axis` is an int, the result is an array of dimension
    ``a.ndim - 1``.  If `axis` is a tuple, the result is an array of
    dimension ``a.ndim - len(axis)``.

See Also
--------
amax :
    The maximum value of an array along a given axis, propagating any NaNs.
nanmin :
    The minimum value of an array along a given axis, ignoring any NaNs.
minimum :
    Element-wise minimum of two arrays, propagating any NaNs.
fmin :
    Element-wise minimum of two arrays, ignoring any NaNs.
argmin :
    Return the indices of the minimum values.

nanmax, maximum, fmax

Notes
-----
NaN values are propagated, that is if at least one item is NaN, the
corresponding min value will be NaN as well. To ignore NaN values
(MATLAB behavior), please use nanmin.

Don't use `~numpy.min` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
``min(a, axis=0)``.

Examples
--------
>>> import numpy as np
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
       [2, 3]])
>>> np.min(a)           # Minimum of the flattened array
0
>>> np.min(a, axis=0)   # Minima along the first axis
array([0, 1])
>>> np.min(a, axis=1)   # Minima along the second axis
array([0, 2])
>>> np.min(a, where=[False, True], initial=10, axis=0)
array([10,  1])

>>> b = np.arange(5, dtype=float)
>>> b[2] = np.nan
>>> np.min(b)
np.float64(nan)
>>> np.min(b, where=~np.isnan(b), initial=10)
0.0
>>> np.nanmin(b)
0.0

>>> np.min([[-50], [10]], axis=-1, initial=0)
array([-50,   0])

Notice that the initial value is used as one of the elements for which the
minimum is determined, unlike for the default argument Python's max
function, which is only used for empty iterables.

Notice that this isn't the same as Python's ``default`` argument.

>>> np.min([6], initial=5)
5
>>> min([6], default=5)
6

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